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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. The following diagram illustrates the solution architecture. The following are some example prompts: Create a new claim.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning - AI

Tools like Terraform and AWS CloudFormation are pivotal for such transitions, offering infrastructure as code (IaC) capabilities that define and manage complex cloud environments with precision. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources.

AWS 98
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Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

AWS Machine Learning - AI

RAG allows models to tap into vast knowledge bases and deliver human-like dialogue for applications like chatbots and enterprise search assistants. Solution overview In this post, we demonstrate how to create a RAG-based application using LlamaIndex and an LLM. Download press releases to use as our external knowledge base.

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The Five Pillars of AWS Well-Architected Framework?—?Understanding What Makes the Cloud Secure

Cloud Conformity

The Five Pillars of AWS Well-Architected Framework?—?Understanding Understanding What Makes the Cloud Secure The 5 pillars to a well-architected house At Cloud Conformity, we often harp on about the AWS Well-Architected Framework and for very good reason. Why should I use the architected framework at all?

AWS 40
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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning - AI

With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. It uses the provided conversation history, action groups, and knowledge bases to understand the context and determine the necessary tasks. The AI assistant interprets the user’s text input.

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Build generative AI agents with Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain

AWS Machine Learning - AI

Amazon Lex supplies the natural language understanding (NLU) and natural language processing (NLP) interface for the open source LangChain conversational agent embedded within an AWS Amplify website. Solution architecture The following diagram illustrates the solution architecture.